A stationary or slow moving object or objects has a similar spectral nature as the surface clutter from its location. Traditionally, it can only be detected by a moving platform radar (airborne, space-based radar, for example) if their returns sufficiently exceed those from the ground (clutter). This requires that its cross-section exceed that of the competing clutter patch established by the range and cross-range resolutions of the radar. In other words, the signal-to-clutter-plus-noise ratio (SCNR) must be significantly greater than zero dB. To recognize an extended target, this requires the SCNR must be high enough in sufficiently many resolution cells. For point targets, the clutter cell size can be reduced arbitrarily, limited only by the performance limits of the radar. For extended targets the object size limits the reduction of the resolution cell for detection processing without over resolving the target.
Of course, both the object and clutter patch may be resolved using wide bandwidth synthetic aperture radar (SAR) processing. This results in an image where some returns from the resolved components of the doubly spread target may exceed those of their respective clutter patch. An “over-resolved” target is defined as one in which the target signal spreads over multiple resolution cells in the range dimension, the Doppler dimension, or n both dimensions. An image is formed and a detection declaration is made by an analyst via a non-coherent, albeit cognitive, process. This imaging process also supports target discrimination. There is no guarantee that the resolution for one section of the extended target is appropriate for other sections and the detection-in-clutter process may not be optimum. For the same reason, the SCNR in each cell may not be optimized for the discrimination process. Even more important, these processes require a man-in-the-loop causing significant delay in the availability of detection declarations while requiring significant communication assets.
Future sensing will be accomplished via unmanned vehicles with limited communications capability. This will require accomplishing the full surveillance mission (detection, track, classification) autonomously.
A technique is proposed to achieve significantly better detection and discrimination for extended targets in clutter. This technique is based upon a generalized inner product (GIP) based approach to data analysis. The GIP was also applied in signal processing to improve the performance of adaptive radars operating in non-homogeneous clutter, by employing multiple templates on limited knowledge of the targets of interest.
The apparatus consists of multiple, iterative GIP analyses, resulting in an improved estimate of the target parameters. It can provide autonomous detection, parameter estimation and discrimination of an extended complex target in colored noise and inhomogeneous clutter.